Application of structured artificial neural networks to computational fluid dynamical problems

摘要

Experimental and computational fluid dynamics(CH)) approaches are now orthodox means in flow prediction. However, in some engineering applications where these approaches are used as a design tool, their expensive and time-consuming nature mayhamper the process of reaching specialists' final goal. In this study, we investigated the potential of applying structured artificial neural networks to fluid dynamical problems. A typical hydraulic flow phenomenon, the Karman vortex street was examinedhere. For realizing the reasoning procedure in this investigation, the sensitivity study of the horizontal velocity profiles on several cross sections over the flow field was conducted. Based on the sensitivity study, three structured neural networks were employed to carry out the flow pattern estimation. They were modeled as the action side of a qualitative rule to work in the reasoning procedure. Compared with the computational fluid dynamical solutions, the estimation accuracy is very encouraging.Furthermore, the proposed reasoning procedure can give a prompt answer compared with those time-consuming conventional approaches.